# Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics

arXiv: https://arxiv.org/abs/2306.10656 · HTML: https://mlsanity.com/paper/2306.10656/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing](https://mlsanity.com/paper/1903.10145/) (2019)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning](https://mlsanity.com/paper/2106.02584/) (2021)
- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models](https://mlsanity.com/paper/1806.06514/) (2018)

## Abstract (arXiv)

Virtual Human Generative Model (VHGM) is a generative model that approximates the joint probability over more than 2000 human healthcare-related attributes. This paper presents the core algorithm, VHGM-MAE, a masked autoencoder (MAE) tailored for handling high-dimensional, sparse healthcare data. VHGM-MAE tackles four key technical challenges: (1) heterogeneity of healthcare data types, (2) probability distribution modeling, (3) systematic missingness in the training dataset arising from multiple data sources, and (4) the high-dimensional, small-n-large-p problem. To address these challenges, VHGM-MAE employs a likelihood-based approach to model distributions with heterogeneous types, a transformer-based MAE to capture complex dependencies among observed and missing attributes, and a novel training scheme that effectively leverages available samples with diverse missingness patterns to mitigate the small-n-large-p problem. Experimental results demonstrate that VHGM-MAE outperforms existing methods in both missing value imputation and synthetic data generation.